################################################################################
# Copyright (c) 2022 Andrej Karpathy

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
################################################################################
import os
import requests
import tiktoken
import numpy as np

# download the tiny shakespeare dataset
input_file_path = os.path.join(os.path.dirname(__file__), "input.txt")
if not os.path.exists(input_file_path):
    data_url = "https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
    with open(input_file_path, "w", encoding="utf-8") as f:
        f.write(requests.get(data_url).text)

with open(input_file_path, encoding="utf-8") as f:
    data = f.read()
n = len(data)
train_data = data[: int(n * 0.9)]
val_data = data[int(n * 0.9) :]

# encode with tiktoken gpt2 bpe
enc = tiktoken.get_encoding("gpt2")
train_ids = enc.encode_ordinary(train_data)
val_ids = enc.encode_ordinary(val_data)
print(f"train has {len(train_ids):,} tokens")
print(f"val has {len(val_ids):,} tokens")

# export to bin files
train_ids = np.array(train_ids, dtype=np.uint16)
val_ids = np.array(val_ids, dtype=np.uint16)
train_ids.tofile(os.path.join(os.path.dirname(__file__), "train.bin"))
val_ids.tofile(os.path.join(os.path.dirname(__file__), "val.bin"))

# train.bin has 301,966 tokens
# val.bin has 36,059 tokens
